Nowadays, huge amounts of location-based data are being shared through the cellular networks with GPS receivers in car navigation systems. The availability of such data opens up new research areas in pattern analysis and data mining. Analyzing individual driving/mobility-patterns from logged GPS data have found a wide range of applications, such as path or destination prediction, real time traffic volume estimation, city planning, energy consumption optimization, etc. In these systems, predictive models are constructed mainly based on statistical properties of data given that it follows some regularity patterns. The patterns can be inferred by analyzing driving history, including routes from origins to destinations.
The current driving route and destination prediction methods are using the history of driving GPS data which can also be connected with additional metadata, e.g. describing characteristics of trip such as driver-id, number of passengers, time-of-say, day-of-week.
However, existing methods may have some drawbacks. Sometimes, statistical properties of data are ignored. That is, the absolute value of predefined observations is used for prediction. Hence, there will be no prediction if the current observation does not totally match the history. In addition, conditions for each person and also between different individuals are weighted equally, which poses another problem. For example, let's consider both time-of-day and day-of-week as two conditions used in the prediction. Both of the conditions could be useful in predicting destinations which are visited based on regular patterns, such as work; however, for many other destinations, e.g. grocery store, it is hard to find such a regularity both in time and day. Hence, the predictability of the model will be reduced if the prior information is not modelled properly and the conditions are equally weighted for all destinations. Additional issue with the existing methods is that they are not flexible in adding or removing features or prior information. And to adapt it with new features, the algorithm should be re-trained again for all recorded data.